trainX Y shape is: (4755, 48, 23) (4755, 60, 23) (4755, 5, 1) Hx= 1 Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a391.py:172 model,normalize = startTrainMult_1H(df,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:400 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:323 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:297 in LSTNetAttention res = add([concat,z]) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:555 in add return Add(**kwargs)(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:463 in __call__ self.build(unpack_singleton(input_shapes)) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:90 in build output_shape = self._compute_elemwise_op_output_shape(output_shape, File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:59 in _compute_elemwise_op_output_shape raise ValueError('Operands could not be broadcast ' ValueError: Operands could not be broadcast together with shapes (23, 32) (5,)

def dp(dist_mat): N, M = dist_mat.shape cost_mat = np.zeros((N + 1, M + 1)) for i in range(1, N + 1): cost_mat[i, 0] = np.inf for i in range(1, M + 1): cost_mat[0, i] = np.inf traceback_mat = np.zeros((N, M)) for i in range(N): for j in range(M): penalty = [ cost_mat[i, j], cost_mat[i, j + 1], cost_mat[i + 1, j]] i_penalty = np.argmin(penalty) cost_mat[i + 1, j + 1] = dist_mat[i, j] + penalty[i_penalty] traceback_mat[i, j] = i_penalty i = N - 1 j = M - 1 path = [(i, j)] while i > 0 or j > 0: tb_type = traceback_mat[i, j] if tb_type == 0: i = i - 1 j = j - 1 elif tb_type == 1: i = i - 1 elif tb_type == 2: j = j - 1 path.append((i, j)) cost_mat = cost_mat[1:, 1:] return (path[::-1], cost_mat)解释一下

cost_mat是一个(N+1)*(M+1)的矩阵，用于存储最小编辑距离。traceback_mat是一个N*M的矩阵，用于记录路径。算法的核心是通过递推的方式，计算出cost_mat和traceback_mat。最后，通过回溯traceback_mat，可以得到最小...

Traceback (most recent call last): File "/root/pythonProject/diffusion.py", line 35, in <module> assert alphas.shape==alphas_prod.shape==alphas_prod_p.shape==alphas_bar_sqrt.shape==one_minus_alphas_bar_logs.shape==one_minus_alphas_sqrt_logs AssertionError

f"Expected {expected_shape}, got {alphas_prod_p.shape}" # [^1] ### 典型形状修复案例在扩散模型中常见形状处理： python # 原始形状 alphas = torch.linspace(0.9, 0.1, steps=1000) # (1000,) # ...

ValueError Traceback (most recent call last) Cell In[16], line 5 3 x = tf.reshape(X_test[t], (1,28,28)) #因为MNIST数据集图片长宽都是28像素，故平铺为一维张量 4 #应用网络模型 ----> 5 y_pred = np.argmax(model.predict(x), axis = 1) 6 plt.subplot(1, 5, i+1) 7 plt.rcParams['font.sans-serif'] = ['SimHei'] File d:\anaconda\envs\tensorflow\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs) 119 filtered_tb = _process_traceback_frames(e.traceback) 120 # To get the full stack trace, call: 121 # keras.config.disable_traceback_filtering() --> 122 raise e.with_traceback(filtered_tb) from None 123 finally: 124 del filtered_tb File d:\anaconda\envs\tensorflow\Lib\site-packages\keras\src\layers\input_spec.py:245, in assert_input_compatibility(input_spec, inputs, layer_name) 243 if spec_dim is not None and dim is not None: 244 if spec_dim != dim: --> 245 raise ValueError( 246 f'Input {input_index} of layer "{layer_name}" is ' 247 "incompatible with the layer: " 248 f"expected shape={spec.shape}, " 249 f"found shape={shape}" 250 ) ValueError: Input 0 of layer "functional_1" is incompatible with the layer: expected shape=(None, 784), found shape=(1, 28, 28)

inputs = Input(shape=(28, 28, 1)) # 接收二维图像输入 --- ### **完整修正代码** python # 加载测试样本 t = np.random.randint(0, len(X_test)) # 随机选择一个测试样本 # 正确预处理 sample = X_test...

torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ openlrm.launch FAILED ------------------------------------------------------------ Failures: [1]: time : 2025-04-30_10:00:39 host : dell-PowerEdge-T640 rank : 2 (local_rank: 2) exitcode : 1 (pid: 34675) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2025-04-30_10:00:39 host : dell-PowerEdge-T640 rank : 3 (local_rank: 3) exitcode : 1 (pid: 34676) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2025-04-30_10:00:39 host : dell-PowerEdge-T640 rank : 5 (local_rank: 5) exitcode : 1 (pid: 34678) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2025-04-30_10:00:39 ho

[00:00:50]: [string "scripts/prefabs/weed_plants.lua"]:116: bad argument #1 to 'random' (interval is empty) LUA ERROR stack traceback: =[C]:-1 in (field) random (C) <-1--1> scripts/prefabs/weed_plants.lua:116 in (upvalue) PlayStageAnim (Lua) <113-124> inst = 120066 - weed_ivy (valid:false) anim = picked custom_pre = nil scripts/prefabs/weed_plants.lua:203 in (field) fn (Lua) <192-207> inst = 120066 - weed_ivy (valid:false) stage = 1 stage_data = table: 000000003A02CC30 scripts/components/growable.lua:179 in (method) SetStage (Lua) <171-181> self = OnLoad = function - ../mods/workshop-2484725102/scripts/upgrade_component.lua:151 growoffscreen = true StartGrowing = function - ../mods/workshop-1645013096/modmain.lua:1 usetimemultiplier = false pausereasons = table: 00000000A3834B40 magicgrowable = true stages = table: 000000003A02DE00 inst = 120066 - weed_ivy (valid:false) stage = 1 domagicgrowthfn = function - scripts/prefabs/weed_plants.lua:357 stage = 1

根据堆栈跟踪信息可以看出，错误发生在“scripts/prefabs/weed_plants.lua”的第116行，具体错误信息是“bad argument #1 to 'random' (interval is empty)”。这个错误提示意味着在调用random函数时，传入的参数...

# 提取特征 y = model(features1) y_fea = model.get_data() # 输出是提取到的特征 y_fea = y_fea.detach().cpu().numpy() # 检查形状 print('y_fea shape before reshape:', y_fea.shape) # 动态计算重塑形状 num_samples = 100 features_per_sample = y_fea.size // num_samples # 检查是否能够整除 if y_fea.size % num_samples != 0: raise ValueError(f"Cannot reshape array of size {y_fea.size} into shape ({num_samples}, -1)") y_fea = y_fea.reshape(num_samples, features_per_sample) print('y_fea shape after reshape:', y_fea.shape) # 获取标签 label = targets.detach().cpu().numpy() # t-sne可视化 t_sne(input_data=y_fea, input_label=label, classes=num_classes)Traceback (most recent call last): File "E:\Anaconda3\CNN\t-sne_CNN.py", line 88, in <module> raise ValueError(f"Cannot reshape array of size {y_fea.size} into shape ({num_samples}, -1)") ValueError: Cannot reshape array of size 3840 into shape (100, -1)

好的，我现在遇到了一个ValueError，提示无法将大小为3840的数组重塑为形状(100, -1)。我需要仔细分析这个问题。首先，代码中的reshape操作是y_fea.reshape(num_samples, features_per_sample)，其中num_samples是...

Traceback (most recent call last): File "C:/yolov5-v6.2/1.py", line 7, in <module> if len(img.shape) == 2: AttributeError: 'NoneType' object has no attribute 'shape'

这个错误通常表示在读取图像文件时出现了问题，导致 img 变量为 None，而 None 没有 shape 属性，因此出现了 AttributeError 的错误。可以检查以下几个方面来解决这个问题： 1. 检查文件路径是否正确：请检查文件...

def convert(size, box): if size[0] != 0: dw = 1. / size[0] else: dw = 0 # 或者你可以选择其他的处理方式 if size[0] != 0: dh = 1. / size[1] else: dw = 0 x = (box[0] + box[1]) / 2.0 y = (box[2] + box[3]) / 2.0 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) 请你根据报错修改这段代码 Traceback (most recent call last): File "D:\hyh\pythonProject\main.py", line 148, in <module> convert_annotation(nameWithoutExtention) # convert label File "D:\hyh\pythonProject\main.py", line 66, in convert_annotation bb = convert((w, h), b) File "D:\hyh\pythonProject\main.py", line 43, in convert y = y * dh UnboundLocalError: local variable 'dh' referenced before assignment 这是报错

根据报错信息，这段代码中的错误是 UnboundLocalError: local variable 'dh' referenced before assignment... y = y * dh h = h * dh return (x, y, w, h) 这样修改后，再次运行代码应该就不会出现该报错了。

%runfile D:/python/zuoye14-2.py --wdir --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File d:\python\zuoye14-2.py:59 56 v_mode1, v_mode2 = Vt[:2] 58 # 时间系数 ---> 59 time_coefficient1 = np.dot(u_mode1.T, sst_matrix) 60 time_coefficient2 = np.dot(u_mode2.T, sst_matrix) 62 # 绘制第一、二模态的空间场 File D:\Anaconda\Lib\site-packages\numpy\lib\_function_base_impl.py:2708, in cov(m, y, rowvar, bias, ddof, fweights, aweights, dtype) 2706 if not rowvar and y.shape[0] != 1: 2707 y = y.T -> 2708 X = np.concatenate((X, y), axis=0) 2710 if ddof is None: 2711 if bias == 0: ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 1836 and the array at index 1 has size 1241

axes[0, 1].imshow(v_mode1[sst_matrix.shape[1]:].reshape(filtered_air.shape[1:]), origin='lower', cmap='coolwarm') axes[0, 1].set_title("AIR Mode 1") # 第二模态 SST 场 axes[1, 0].imshow(u_mode2[:sst_...

VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV Unable to stop the stream: Device or resource busy Traceback (most recent call last): File "3_d.py", line 27, in <module> if img.shape[2] == 1: AttributeError: 'NoneType' object has no attribute 'shape'

if img.shape[2] == 1: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) elif img.shape[2] == 4: img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR) # 转换像素格式 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #...

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [27], in <cell line: 11>() 9 model.add(LSTM(units=32, input_shape=(sequence_length, 4))) 10 model.add(Dropout(0.2)) ---> 11 model.add(LSTM(units=32)) 12 model.add(Dense(units=1, activation='sigmoid')) 14 # 编译模型 File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/trackable/base.py:204, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 202 self._self_setattr_tracking = False # pylint: disable=protected-access 203 try: --> 204 result = method(self, *args, **kwargs) 205 finally: 206 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File ~/anaconda3/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~/anaconda3/lib/python3.9/site-packages/keras/src/engine/input_spec.py:235, in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " 238 f"expected ndim={spec.ndim}, found ndim={ndim}. " 239 f"Full shape received: {tuple(shape)}" 240 ) 241 if spec.max_ndim is not None: 242 ndim = x.shape.rank ValueError: Input 0 of layer "lstm_8" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 32)

这个错误提示说明你的 LSTM 层期望输入的张量维度为 3，但是实际的输入张量维度只有 2。具体来说，LSTM 层期望的输入张量维度应该为 (batch_size, sequence_length, input_dim)，其中 batch_size 表示输入的样本数目...

Traceback (most recent call last): File "E:/360zip/pythonProject/pythonProject/pythonProjectdaodao/demo1.py", line 8, in <module> height, width, channels = image1.shape ValueError: not enough values to unpack (expected 3, got 2)什么意思

这个错误通常意味着图像的形状（shape）不是您所期望的形状。在这种情况下，您期望的形状是一个三元组（height，width，channels），但是实际上您得到的是一个二元组（height，width），因为该图像可能是单通道...

那这里有一个例子 Features shape: torch.Size([14196, 10, 5]) Filters shape: torch.Size([3, 3, 3, 5, 16]) Indice pairs shape: torch.Size([27, 2, 14196]) Num activate out: 14196 ==== getitem 返回项的 shape (帧 561) ===== points shape: torch.Size([8000, 5]) radar shape: torch.Size([1, 576, 1152]) camera shape: torch.Size([1, 3, 376, 672]) gt_bboxes_3d shape: (200, 7) gt_labels_3d shape: (200,) lidar2ego shape: (4, 4) camera2ego shape: (4, 4) camera_intrinsics shape: (3, 3) camera2lidar shape: (4, 4) radar2lidar shape: (4, 4) img_aug_matrix shape: (4, 4) lidar_aug_matrix shape: (4, 4) metadata: {'frame_id': 561, 'lidar_count': 23451, 'radar_max': 156.0, 'camera_bboxes_count': 0} Traceback (most recent call last): File "train.py", line 105, in <module> main() File "train.py", line 94, in main train_model( File "/home/nieruifeng/ContextualFusion-master/mmdet3d/apis/train.py", line 155, in train_model runner.run(data_loaders, [("train", 1)]) File "/home/nieruifeng/miniconda3/envs/weather/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], kwargs) File "/home/nieruifeng/ContextualFusion-master/mmdet3d/runner/epoch_based_runner.py", line 17, in train super().train(data_loader, kwargs) File "/home/nieruifeng/miniconda3/envs/weather/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train self.run_iter(data_batch, train_mode=True, **kwargs) File "/home/nieruifeng/miniconda3/envs/weather/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter outputs = self.model.train_step(data_batch, self.optimizer, File "/home/nieruifeng/miniconda3/envs/weather/lib/python3.8/site-packages/mmcv/parallel/distributed.py", line 52, in train_step output = self.module.train_step(*inputs[0], kwargs[0]) File "/home/nieruifeng/ContextualFusion-master/mmdet3d/models/fusion_models/base.py", line 78, in train_step losses = self(data) Fi

# Input shape: (N, 4) [x,y,z,反射强度] # 错误配置：point_cloud_dim=3 # 正确配置：point_cloud_dim=4 2. **标注数据维度异常** - 检查标注文件是否包含完整的3D边界框参数： $$(x, y, z, dx, dy, dz,...

上述代码报错：Traceback (most recent call last): File "Z:\coding\Project\cup_detect\pj.py", line 59, in <module> result = vertical_stitch(images) File "Z:\coding\Project\cup_detect\pj.py", line 43, in vertical_stitch combined[y:end, :] = blended[overlap:] ValueError: could not broadcast input array from shape (0,256,3) into shape (20,256,3)

好的，用户遇到了一个numpy的广播错误，具体错误信息是ValueError: could not broadcast input array from shape (0,256,3) into shape (20,256,3)。我需要分析这个问题并提供解决方案。首先，这个错误通常发生在...

import tensorflow as tf import numpy as np from keras import Model from keras.layers import * from sklearn.model_selection import train_test_split in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") in_flow = Reshape(in_flow, (D1.shape[0], 5, 109, 109)) out_flow = Reshape(out_flow, (D1.shape[0], 5, 109)) c1 = Reshape(c1, (D1.shape[0], 5, 109)) X_train, X_test, y_train, y_test = train_test_split((in_flow, out_flow, c1), D1, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train,y_train, test_size=0.2, random_state=42) input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error') history = model.fit([X_train[:,0:5,:,:], X_train[:,5:10,:], X_train[:,10:15,:]], y_train, validation_data=([X_val[:,0:5,:,:], X_val[:,5:10,:], X_val[:,10:15,:]], y_val), epochs=10, batch_size=32) test_loss = model.evaluate([X_test[:,0:5,:,:], X_test[:,5:10,:], X_test[:,10:15,:]], y_test) print("Test loss:", test_loss) 程序的运行结果为Traceback (most recent call last): File "C:\Users\liaoshuyu\Desktop\python_for_bigginer\5.23.py", line 11, in <module> in_flow = Reshape(in_flow, (D1.shape[0], 5, 109, 109)) TypeError: Reshape.init() takes 2 positional arguments but 3 were given 怎么修改

in_flow = np.reshape(in_flow, (D1.shape[0], 5, 109, 109)) out_flow = np.reshape(out_flow, (D1.shape[0], 5, 109)) c1 = np.reshape(c1, (D1.shape[0], 5, 109)) 这样就可以正确地将数据重塑为指定的形状...

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Traceback (most recent call last): File "/root/pythonProject/diffusion.py", line 35, in <module> assert alphas.shape==alphas_prod.shape==alphas_prod_p.shape==alphas_bar_sqrt.shape==one_minus_alphas_bar_logs.shape==one_minus_alphas_sqrt_logs AssertionError

Traceback (most recent call last): File "C:/yolov5-v6.2/1.py", line 7, in <module> if len(img.shape) == 2: AttributeError: 'NoneType' object has no attribute 'shape'

VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV Unable to stop the stream: Device or resource busy Traceback (most recent call last): File "3_d.py", line 27, in <module> if img.shape[2] == 1: AttributeError: 'NoneType' object has no attribute 'shape'

Traceback (most recent call last): File "E:/360zip/pythonProject/pythonProject/pythonProjectdaodao/demo1.py", line 8, in <module> height, width, channels = image1.shape ValueError: not enough values to unpack (expected 3, got 2)什么意思

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